With the development of digital media, the amount of digital images has grown exponentially; especially in electronic Internet, all details of commodities for sale are exhibited with images of the commodities, wherein the images with rich semantic contents replace descriptions of the details of the commodities. So the amount of images is being increasing. And how to categorize a large scale of image data according to the commodities described in the images becomes an urgent problem.
Existing image object category recognition methods mostly use machine learning methods. In practice, parameters in a majority of learning models are obtained through training samples, and have uncertainties. At the same time, due to differences in the training samples, classification models will produce errors, and errors and error rates are present in attribution of the object category. In addition, although part of the framework for object recognition using a multi-layer structure improves recognition accuracy, it requires a lot of resources and spends a lot of time in recognizing category.